1. Technical Field
The present exemplary embodiment relates to a data processing apparatus, a medical diagnostic apparatus, a data processing method and a medical diagnostic method which improve SNR (signal to noise ratio) by reducing noise of time axis and/or spatial axis data having random noise, and more particularly, to a data processing apparatus, a medical diagnostic apparatus, a data processing method and a medical diagnostic method which correct the data adaptively to SNR so as to reduce noise selectively while yet keeping data of a high frequency part and/or a high SNR part.
2. Description of the Related Art
Conventionally, filtering is performed for reducing random noise in data having space axes and a temporal axis. Filters for reducing noise include an adaptive filter of which filter strength is determined according to data in addition to a linear filter of which filter strength does not change temporally and spatially. A structure adaptive filter and SNR adaptive filter are proposed as an adaptive filter to reduce random noise spatially and temporally.
The structure adaptive filter is a filter of which filter strength is determined according to structure of data to maintain a local structure of a high-frequency component such as an edge, a line, or a point. A type which detects directions of an edge and a line and controls a direction of filtering according to the detected directions of an edge and a line, and a type which controls a filter strength are included in structure adaptive filters.
For example, a filter called a sigma filter is known as a structure adaptive filter which controls filter strength according to an edge detected from image data. The sigma filter is a filter which generates a weighting function from data derived by enhancing an intermediate-frequency component or a high-frequency component in image data and which reduces noise while yet preserving the edge in the image data by weighted addition of image data and data derived by enhancing the intermediate-frequency component or the high-frequency component with the generated weighting function. The sigma filter is a filter which performs so-called edge preservation or edge enhancement. The correction processing (filtering) of data by the sigma filter can be represented as expression (1-1) and expression (1-2) when original data at one-dimensional position (x) to be a target of the filtering is Sorig(x), high-frequency component (high pass filtered data) obtained by applying a high pass filter (HPF) to the original data Sorig(x) is Shigh(x), low-frequency component (low pass filtered data) obtained by applying a low pass filter (LPF) to the original data Sorig(x) is Slow(x), a weighting function is Whigh(x), and corrected data after filtering is Scor(x).Whigh(x)=Shigh(x)/max[Shigh(x)]  (1-1)Scor(x)=Whigh(x)*Sorig(x)+{1−Whigh(x)}Slow(x)  (1-2)
That is, as shown in expression (1-1), the high-frequency component Shigh(x) is extracted as the edge part of the original data Sorig(x) and the extracted high-frequency component Shigh(x) is normalized by the maximum value max[Shigh(x)] of the high-frequency component Shigh(x). Then, the normalized high-frequency component is set as the weighting function Whigh(x). Subsequently, the corrected data Scor(x) is obtained by weighted addition of the original data Sorig(x) and the low-frequency component Slow(x) which is smoothing data with the weighting function Whigh(x).
On the other hand, the SNR adaptive filter is a filter which optimizes filter strength according to SNR of data. A Wiener Filter (WF) is proposed as a specific example of the SNR adaptive filter. More specifically, a Fourier WF (FTW) operating in normal frequency space and a FREBAS WF (FRW) operating in FREBAS space obtained by band division with Fresnel transform are proposed (for example, refer to Ito S., Yamada Y. “Use of Dual Fresnel Transform Pairs to Improve Signal-to-Noise Ratio in Magnetic Resonance Imaging,” Med. Imag. Tech., 19 (5), 355-369 (2001)).
However, the proposed conventional FTW is a filter which improves SNR of data by processing in frequency space. Generally, a noise component (N) is approximately constant in a frequency space. However, since a signal component in higher frequency is more reduced, deterioration in high-frequency component of data cannot be avoided when SNR correction of the data is performed with the WF. On the other hand, since FREBAS space maintains a certain amount of space information, FRW can preserve high-frequency components including an edge in some degree compared to FTW. However, there is the problem that FRW does not operate adaptively to the SNR of the low-frequency component. Thus, SNR adaptive filter which operates adaptively according to SNR space distribution over a wide frequency band is not especially proposed.
SNR depends on not only frequency of data, but also position. That is, SNR is not uniform in real data space, is larger at a higher signal part and is smaller at a lower signal part.
There is the case that SNR is affected by processing in a display system to display data visually.
Furthermore, there are some data, derived by image processing in various modalities or each modality, of which values do not positively correlate with SNR. Especially, an example of data without positive correlation relationship between data value and SNR is processing data such as CT values obtained in an X-ray computed tomography (CT) apparatus and apparent diffusion coefficients (ADC) obtained in a magnetic resonance imaging (MRI) apparatus.
Note that a diffusion weighted signal for obtaining ADC changes according to a gradient magnetic field factor b and shows negative correlation relation with SNR. However, ADC is calculated from signal intensity S(b) of a diffusion weighted signal with expression (2). Therefore, when the signal intensity S(b) of the diffusion weighted signal increases in the case of S(b)<S(0), the ADC value becomes small. That is, SNR of ADC shows nonlinear correlation to SNR of signal intensity S(b) of a diffusion weighted signal. The SNR of ADC shows a peak when S(0)/S(b)=3 in relation with a diffusion weighted signal S(b). Furthermore, the SNR of ADC shows a peak when b×ADC=1.1 in relation with ADC value.ADC=ln {S(0)/S(b)}/b  (2)
Therefore, optimization processing method of SNR is different between the case that a data value and SNR have a positive correlation relationship and the case that a data value and SNR do not have a positive correlation relationship. However, today, a filter considering whether or not a value of data and SNR have positive correlation relationship is not proposed.